ORCID iD
Wickramarachchi: 0000-0001-5810-1849
Henson: 0000-0003-3875-3705
Sheth: 0000-0002-0021-5293
Document Type
Article
Abstract
Knowledge graph completion (KGC) is a problem of significant importance due to the inherent incompleteness in knowledge graphs (KGs). The current approaches for KGC using link prediction (LP) mostly rely on a common set of benchmark datasets that are quite different from real-world industrial KGs. Therefore, the adaptability of current LP methods for real-world KGs and domain-specific ap- plications is questionable. To support the evaluation of current and future LP and KGC methods for industrial KGs, we introduce DSceneKG, a suite of real-world driving scene knowledge graphs that are currently being used across various industrial applications. The DSceneKG is publicly available at: https://github.com/ruwantw/DSceneKG.
Publication Info
Preprint version The 23rd International Semantic Web Conference (ISWC), 2024.
APA Citation
Wickramarachchi, R., Henson, C., & Sheth, A. (2024). A benchmark knowledge graph of driving scenes for knowledge completion tasks. The 23rd International Semantic Web Conference (ISWC). [Preprint]
Rights
2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).